Real-Time Semantic Segmentation
86 papers with code • 8 benchmarks • 12 datasets
Semantic Segmentation is a computer vision task that involves assigning a semantic label to each pixel in an image. In Real-Time Semantic Segmentation, the goal is to perform this labeling quickly and accurately in real-time, allowing for the segmentation results to be used for tasks such as object recognition, scene understanding, and autonomous navigation.
( Image credit: TorchSeg )
Benchmarks
These leaderboards are used to track progress in Real-Time Semantic Segmentation
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Real-Time Semantic Segmentation models and implementationsDatasets
Cityscapes
NYUv2
COCO-Stuff
CamVid
Kvasir-SEG
Kvasir
Kvasir-Instrument
FLAME
Medico automatic polyp segmentation challenge (dataset)
KvasirCapsule-SEG
Latest papers
Multi-Level Aggregation and Recursive Alignment Architecture for Efficient Parallel Inference Segmentation Network
Real-time semantic segmentation is a crucial research for real-world applications.
SCTNet: Single-Branch CNN with Transformer Semantic Information for Real-Time Segmentation
xzz777/sctnet
•
•
Recent real-time semantic segmentation methods usually adopt an additional semantic branch to pursue rich long-range context.
Bilateral Network with Residual U-blocks and Dual-Guided Attention for Real-time Semantic Segmentation
To be precise, we use the Dual-Guided Attention (DGA) module we proposed to replace some multi-scale transformations with the calculation of attention which means we only use several attention layers of near linear complexity to achieve performance comparable to frequently-used multi-layer fusion.
AsymFormer: Asymmetrical Cross-Modal Representation Learning for Mobile Platform Real-Time RGB-D Semantic Segmentation
Understanding indoor scenes is crucial for urban studies.
Spatial-Assistant Encoder-Decoder Network for Real Time Semantic Segmentation
To ascertain the effectiveness of our approach, our SANet model achieved competitive results on the real-time CamVid and cityscape datasets.
JetSeg: Efficient Real-Time Semantic Segmentation Model for Low-Power GPU-Embedded Systems
The JetNet is designed for GPU-Embedded Systems and includes two main components: a new light-weight efficient block called JetBlock, that reduces the number of parameters minimizing memory usage and inference time without sacrificing accuracy; a new strategy that involves the combination of asymmetric and non-asymmetric convolutions with depthwise-dilated convolutions called JetConv, a channel shuffle operation, light-weight activation functions, and a convenient number of group convolutions for embedded systems, and an innovative loss function named JetLoss, which integrates the Precision, Recall, and IoUB losses to improve semantic segmentation and reduce computational complexity.
Real-Time Semantic Segmentation using Hyperspectral Images for Mapping Unstructured and Unknown Environments
In our work we propose the use of hyperspectral images for real-time pixel-wise semantic classification and segmentation, without the need of any prior training data.
COVERED, CollabOratiVE Robot Environment Dataset for 3D Semantic segmentation
Despite the importance of semantic understanding for such applications, 3D semantic segmentation of collaborative robot workspaces lacks sufficient research and dedicated datasets.
Lightweight Real-time Semantic Segmentation Network with Efficient Transformer and CNN
iviplab/letnet
•
•
In the past decade, convolutional neural networks (CNNs) have shown prominence for semantic segmentation.
Uncertainty in Real-Time Semantic Segmentation on Embedded Systems
ethangoan/eu-seg
•
•
Application for semantic segmentation models in areas such as autonomous vehicles and human computer interaction require real-time predictive capabilities.
